<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Nicolas Kourtellis</style></author><author><style face="normal" font="default" size="100%">Jeremy Blackburn</style></author><author><style face="normal" font="default" size="100%">Emiliano De Cristofaro</style></author><author><style face="normal" font="default" size="100%">Gianluca Stringhini</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mean Birds: Detecting Aggression and Bullying on Twitter</style></title><tertiary-title><style face="normal" font="default" size="100%">WebSci '17</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/1702.06877</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Troy, NY, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In recent years, bullying and aggression against users on social media have grown significantly, causing serious consequences to victims of all demographics. In particular, cyberbullying affects more than half of young social media users worldwide, and has also led to teenage suicides, prompted by prolonged and/or coordinated digital harassment. Nonetheless, tools and technologies for understanding and mitigating it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of cyberbullies and aggressors, and what features distinguish them from regular users. We find that bully users post less, participate in fewer online communities, and are less popular than normal users, while aggressors are quite popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, achieving over 90% AUC.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Nicolas Kourtellis</style></author><author><style face="normal" font="default" size="100%">Jeremy Blackburn</style></author><author><style face="normal" font="default" size="100%">Emiliano De Cristofaro</style></author><author><style face="normal" font="default" size="100%">Gianluca Stringhini</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 26th International Conference on World Wide Web Companion</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">WWW '17 Companion</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dl.acm.org/citation.cfm?id=3053890</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Perth, Australia</style></pub-location><pages><style face="normal" font="default" size="100%">1285-1290</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Over the past few years, online aggression and abusive behaviors have occurred in many different forms and on a variety of platforms. In extreme cases, these incidents have evolved into hate, discrimination, and bullying, and even materialized into real-world threats and attacks against individuals or groups. In this paper, we study the Gamergate controversy. Started in August 2014 in the online gaming world, it quickly spread across various social networking platforms, ultimately leading to many incidents of cyberbullying and cyberaggression. We focus on Twitter, presenting a measurement study of a dataset of 340k unique users and 1.6M tweets to study the properties of these users, the content they post, and how they differ from random Twitter users. We find that users involved in this &quot;Twitter war&quot; tend to have more friends and followers, are generally more engaged and post tweets with negative sentiment, less joy, and more hate than random users. We also perform preliminary measurements on how the Twitter suspension mechanism deals with such abusive behaviors. While we focus on Gamergate, our methodology to collect and analyze tweets related to aggressive and bullying activities is of independent interest.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Paschalis Korosoglou</style></author><author><style face="normal" font="default" size="100%">Pavlos Daoglou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A multi-layer software architecture framework for adaptive real-time analytics</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop on Real-time &amp; Stream Analytics in Big Data</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">big data analytics</style></keyword><keyword><style  face="normal" font="default" size="100%">cloud based services</style></keyword><keyword><style  face="normal" font="default" size="100%">real time data management</style></keyword><keyword><style  face="normal" font="default" size="100%">software architecutures</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><pub-location><style face="normal" font="default" size="100%">Washington D.C.</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Highly distributed applications dominate today’s software industry posing new challenges for novel software architectures capable of supporting real time processing and analytics. The proposed framework, so called REAλICS, is motivated by the fact that the demand for aggregating current and past big data streams requires new software methodologies, platforms and services. The proposed framework is designed to tackle with data intensive problems in real time environments, via services built dynamically under a fully scalable and elastic Lambda based architecture. REAλICS proposes a multi-layer software platform, based on the lambda architecture paradigm, for aggregating and synchronizing real time and batch processing. The proposed software layers and adaptive components support quality of experience, along with community driven software development. Flexibility and elasticity are targeted by hiding the complexity of bootstrapping and maintaining a multi level architecture, upon which the end user can drive queries over input data streams. REAλICS proposes a flexible and extensible software architecture that can capture&lt;br /&gt;
users preference at the front-end and adapHighly distributed applications dominate today’s software industry posing new challenges for novel software architectures capable of supporting real time processing and analytics. The proposed framework, so called REAλICS, is motivated by the fact that the demand for aggregating current and past big data streams requires new software methodologies, platforms and services. The proposed framework is designed to tackle with data intensive problems in real time environments, via services built dynamically under a fully scalable and elastic Lambda based architecture. REAλICS proposes a multi-layer software platform, based on the lambda architecture paradigm, for aggregating and synchronizing real time and batch&lt;br /&gt;
processing. The proposed software layers and adaptive components support quality of experience, along with community&lt;br /&gt;
driven software development. Flexibility and elasticity are targeted by hiding the complexity of bootstrapping and maintaining a multi level architecture, upon which the end user can drive queries over input data streams. REAλICS proposes a flexible and extensible software architecture that can capture users preference at the front-end and adapt the appropriate distributed technologies and processes at the back-end. Such a model enables real time analytics in large-scale data driven cloud-based systems.t the appropriate distributed technologies and processes at the back-end. Such a model enables real time analytics in large-scale data driven cloud-based systems.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Passalis, Nikolaos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Sanjay Kumar Madria</style></author><author><style face="normal" font="default" size="100%">Hara, Takahiro</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">MultiSpot: Spotting Sentiments with Semantic Aware Multilevel Cascaded Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Big Data Analytics and Knowledge Discovery</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Multilevel features</style></keyword><keyword><style  face="normal" font="default" size="100%">Sentiment detection</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-22729-0_26</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><volume><style face="normal" font="default" size="100%">9263</style></volume><pages><style face="normal" font="default" size="100%">337-350</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-22728-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Konstantinos Kafetsios</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Micro-blogging Content Analysis via Emotionally-Driven Clustering</style></title><secondary-title><style face="normal" font="default" size="100%">Affective Computing and Intelligent Interaction (ACII), 2013 Humaine Association Conference on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">affective analysis methodology</style></keyword><keyword><style  face="normal" font="default" size="100%">Clustering algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">content management</style></keyword><keyword><style  face="normal" font="default" size="100%">content sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">Dictionaries</style></keyword><keyword><style  face="normal" font="default" size="100%">emotion intensity monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">emotionally-driven clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">Equations</style></keyword><keyword><style  face="normal" font="default" size="100%">human emotion states</style></keyword><keyword><style  face="normal" font="default" size="100%">information sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">lexicon-based technique</style></keyword><keyword><style  face="normal" font="default" size="100%">Mathematical model</style></keyword><keyword><style  face="normal" font="default" size="100%">microblogging content analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">pattern clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">people perception</style></keyword><keyword><style  face="normal" font="default" size="100%">Pragmatics</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantics</style></keyword><keyword><style  face="normal" font="default" size="100%">Sentiment analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">social networking (online)</style></keyword><keyword><style  face="normal" font="default" size="100%">social pulse</style></keyword><keyword><style  face="normal" font="default" size="100%">social relations</style></keyword><keyword><style  face="normal" font="default" size="100%">text analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Twitter</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">375-380</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Mani-Web: Large-Scale Web Graph Embedding via Laplacian Eigenmap Approximation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Web as a graph can be embedded in a lowdimensionalspace where its geometry can be visualized and studiedin order to mine interesting patterns such as web communities.The existing algorithms operate on small-to-medium-scalegraphs; thus, we propose a close to linear time algorithm calledMani-Web suitable for large-scale graphs. The result is similarto the one produced by the manifold-learning technique Laplacianeigenmap that is tested on artificial manifolds and real webgraphs.Mani-Web can also be used as a general-purpose manifoldlearning/dimensionality-reductiontechnique as long as the datacan be represented as a graph.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stamos, Konstantinos</style></author><author><style face="normal" font="default" size="100%">Laskaris, Nikolaos A.</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mani-Web: Large-Scale Web Graph Embedding via Laplacian Eigenmap Approximation</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Systems, Man, and Cybernetics, Part C</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Laplacian eigenmap</style></keyword><keyword><style  face="normal" font="default" size="100%">large scale</style></keyword><keyword><style  face="normal" font="default" size="100%">manifold learning</style></keyword><keyword><style  face="normal" font="default" size="100%">spectral graph theory</style></keyword><keyword><style  face="normal" font="default" size="100%">web communities</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">879-888</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Web as a graph can be embedded in a lowdimensionalspace where its geometry can be visualized and studiedin order to mine interesting patterns such as web communities.The existing algorithms operate on small-to-medium-scalegraphs; thus, we propose a close to linear time algorithm calledMani-Web suitable for large-scale graphs. The result is similarto the one produced by the manifold-learning technique Laplacianeigenmap that is tested on artificial manifolds and real webgraphs.Mani-Web can also be used as a general-purpose manifoldlearning/dimensionality-reductiontechnique as long as the datacan be represented as a graph.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maria Giatsoglou</style></author><author><style face="normal" font="default" size="100%">Symeon Papadopoulos</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Jain, Lakhmi C.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Massive Graph Management for the Web and Web 2.0</style></title><secondary-title><style face="normal" font="default" size="100%">New Directions in Web Data Management 1</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Studies in Computational Intelligence</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">331</style></volume><pages><style face="normal" font="default" size="100%">19-58</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-17550-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Moussiades, Lefteris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Kefalas, Petros</style></author><author><style face="normal" font="default" size="100%">Stamatis, Demosthenes</style></author><author><style face="normal" font="default" size="100%">Douligeris, Christos</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Mining the Community Structure of a Web Site</style></title><secondary-title><style face="normal" font="default" size="100%">BCI</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">239-244</style></pages><isbn><style face="normal" font="default" size="100%">978-0-7695-3783-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pallis, George</style></author><author><style face="normal" font="default" size="100%">Angelis, Lefteris</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hacid, Mohand-Said</style></author><author><style face="normal" font="default" size="100%">Murray, Neil V.</style></author><author><style face="normal" font="default" size="100%">Ras, Zbigniew W.</style></author><author><style face="normal" font="default" size="100%">Tsumoto, Shusaku</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Model-Based Cluster Analysis for Web Users Sessions</style></title><secondary-title><style face="normal" font="default" size="100%">ISMIS</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Model-Based Cluster Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3488</style></volume><pages><style face="normal" font="default" size="100%">219-227</style></pages><isbn><style face="normal" font="default" size="100%">3-540-25878-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">One of the main issues in Web usage mining is the discovery of patternsin the navigational behavior of Web users. Standard approaches, such as clusteringof usersâ€™sessions and discovering association rules or frequent navigational paths,do not generally allow to characterize or quantify the unobservable factors that leadto common navigational patterns. Therefore, it is necessary to develop techniquesthat can discover hidden and useful relationships among users as well as betweenusers and Web objects.Correspondence Analysis(CO-AN) is particularly useful inthis context, since it can uncover meaningful associations among users and pages.We present a model-based cluster analysis for Web users sessions including anovel visualization and interpretation approach which is based on CO-AN.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Model-Based Cluster Analysis for Web Users Sessions</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;One of the main issues in Web usage mining is the discovery of patternsin the navigational behavior of Web users. Standard approaches, such as clusteringof users’sessions and discovering association rules or frequent navigational paths,do not generally allow to characterize or quantify the unobservable factors that leadto common navigational patterns. Therefore, it is necessary to develop techniquesthat can discover hidden and useful relationships among users as well as betweenusers and Web objects.Correspondence Analysis(CO-AN) is particularly useful inthis context, since it can uncover meaningful associations among users and pages.We present a model-based cluster analysis for Web users sessions including anovel visualization and interpretation approach which is based on CO-AN.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Hacid, Mohand-Said</style></author><author><style face="normal" font="default" size="100%">Elmagarmid, Ahmed K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MPEG-7 based description schemes for multi-level video content classification</style></title><secondary-title><style face="normal" font="default" size="100%">Image Vision Comput.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">367-378</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">The MPEG-7 Multimedia Content Description Standard and the XML Schema Language</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Web accessed multimedia applications have been widelyadopted under enhanced requirements for fast searching,browsing and retrieval. Efficient multimedia data contentdescription is a neccesary and critical issue towards whichseveral solutions have been proposed. In the present paper,we focus on the MPEG-7 standard as an efficient multimediacontent description tool. MPEG-7 is a newly introducedstandard from the MPEG commitee and a brief descriptionof the standard and its components is given here. MPEG-7 Language-Schema as used towards efficient Audio/Visualcontent description is discussed more extensively. As decidedby the MPEG-7 committee it the Description DefinitionLanguage (DDL) of the standard should be based onthe XML Schema. The several extensions to this schema,necessary for satisfying the requirements of MPEG-7 arediscussed, while other functionalities of the schema whichseem not necessary under current specifications are alsocommented.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Multimedia Data Elevation under a Hierarchical Storage Model</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Multimedia data storage is a critical issue in large scale applications.This paper proposes a frequency based multimedia data representationmodel which effectively guides data storage and elevation among the secondaryand tertiary storage levels. Multimedia data are stored on the tertiary storagelevel and (based on certain popularity criteria) they are elevated on secondarylevel towards improving both the request servicing and the data’s accessibility.The proposed multimedia data elevation is a prefetching approach since it isperformed “a priori” (not on demand) based on available information on usersaccess patterns. Secondary storage placement is performed by the use of twodistinct type placement policies, namely the “Constructive Placement” and the“Iterative Improvement” algorithms. A simulation model has been developedto evaluate the proposed hierarchical data model and the applied placementstrategies. Experimentation results have shown that the this hierarchical approachunder the iterative improvement placement outperforms earlier relatedmultimedia data placement policies.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Terzi, Evimaria</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multimedia Data Storage and Representation Issues on Tertiary Storage Subssystems: An Overview</style></title><secondary-title><style face="normal" font="default" size="100%">Operating Systems Review</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">61-77</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Multimedia documents Storage An Evolutionary based Application</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Multimedia data storage is a critical issue in relation to the overal l systems performanceand functionality This paper studies a multimediadocument application which eectively guides dataplacement towards improving the quality of presentation of multimedia data Several storage policiesare proposed towards better response and servicetimes Multimedia data dependencies access frequencies and timing constraints are used to guidethe storage policies under a certain representation model The proposed placement policies are basedon the simulated annealing algorithm and an extended improved version of the typical simulated annealing approach is presented Experimentation results are presented and their impact on the total systems performance is commented and evaluated.&lt;/p&gt;
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